Fuzzy Modelling for Prediction of Bursting Strength of Knitted Cotton Fabric using Bleaching Process Variables

Abstract

A fuzzy prediction model has been built based on hydrogen peroxide concentration, bleaching temperature, and time of bleaching as the input variables and knitted cotton fabric bursting strength as the output variable. Fuzzy expert systems can map efficiently in nonlinear domains with minimal experimental data. The model developed in the present study has been validated by new experimental data. The root mean square, mean absolute error percentage, and coefficient of determination ( R 2 ) between the predicted and experimental values were found to be 4.89, 0.707, and 0.965 respectively. The results confirm that the model can be applied successfully for the prediction of fabric bursting strength in textile dye houses.

Prognosis of Dimensional Stability and Mass per Unit Area of Single Jersey Cotton Knitted Fabric with Fuzzy Inference System

“…Mamdani max-min fuzzy inference mechanism assures a linear interpolation of outputs between the rules [24,25]. Therefore, the model is constructed by using this mechanism.…”

“…Now, based on diff erent parameters, a lot of work has been done by many researchers to forecast knitted fabric attributes, e.g. bursting strength, pilling tendency, air permeability, thermal resistance, spirality, hand evaluation, defect classifi cation etc, and also in the woven and apparel industry [24][25][26][27][28][29][30]. An enormous amount of noise free data is required in establishing the ANN and ANFIS model, which becomes very diffi cult and time consuming to accumulate from the textile industry [22,23,31].…”

“…Conversely, the fuzzy inference system is a very profi cient modelling tool which overcomes the lacunas of ANN, ANFIS and statistical regression, as it is based on fuzzy logic that can easily interpret the knowledge of experts into a set of rules in an inference system and is focused on modes of reasoning that are fairly accurate rather than exact. Th e fuzzy expert system implementation is easier than other models, since it can execute the modelling operation with a small amount of experimental data in the non-linear, imprecise, vague, trial-error and complex textile domain [22][23][24][25][31][32][33]. Th erefore, in this current research, the predictions of mass per unit area and shrinkage properties of a plain cotton knitted fabric were performed by constructing a fuzzy knowledge-based model as it is more userfriendly, of low design cost and simply applicable.…”

Improvement of Elastic Property of Circular Weft Knit Three-Thread Fleece Fabric by Changing Stitch Length

“…Demand for knit fabrics has grown gradually since the 1970s due to some of its special properties, such as comfortability, easy-care, stretchability, excellent wrinkle resistance, and fashion features. [1][2][3] Single jersey cotton knit fabric has the attribute of a soft hand, which makes it skin-friendly and comfortable to wear. 4 Different types of single jersey knit fabric (commonly called derivatives) are available in the market, such as pique, lacoste, normal terry, cross miss, fleece, and so forth.…”

Evaluation of knitted suit fabric style based on fuzzy neural network

“…Shabaridharan and Das 8 developed an artificial neural network model to predict the thermal and evaporative resistance of multilayered fabrics and compared with the experimental values. Haque et al 9 built a fuzzy prediction model based on hydrogen peroxide concentration. In order to build up a holistic picture of purchase preferences, Čubrić et al 10 studied to conduct an in-depth analysis of customer preferences for single jersey knit fabrics regarding the attributes of smoothness and softness.…”